Modelling retrieval models in a probabilistic relational algebra with a new operator: the relational Bayes

Modelling retrieval models in a probabilistic relational algebra with a new operator: the... This paper presents a probabilistic relational modelling (implementation) of the major probabilistic retrieval models. Such a high-level implementation is useful since it supports the ranking of any object, it allows for the reasoning across structured and unstructured data, and it gives the software (knowledge) engineer control over ranking and thus supports customisation. The contributions of this paper include the specification of probabilistic SQL (PSQL) and probabilistic relational algebra (PRA), a new relational operator for probability estimation (the relational Bayes), the probabilistic relational modelling of retrieval models, a comparison of modelling retrieval with traditional SQL versus modelling retrieval with PSQL, and a comparison of the performance of probability estimation with traditional SQL versus PSQL. The main findings are that the PSQL/PRA paradigm allows for the description of advanced retrieval models, is suitable for solving large-scale retrieval tasks, and outperforms traditional SQL in terms of abstraction and performance regarding probability estimation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Modelling retrieval models in a probabilistic relational algebra with a new operator: the relational Bayes

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Publisher
Springer-Verlag
Copyright
Copyright © 2007 by Springer-Verlag
Subject
Computer Science; Database Management
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-007-0073-y
Publisher site
See Article on Publisher Site

References

  • Probabilistic models of information retrieval based on measuring the divergence from randomness
    Amati, G.; van Rijsbergen, C.J.

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